The topic of multi-modal biometrics has attracted great interest in recent years. This talk will categorize different approaches to multi-modal biometrics based on the biometric sources, the type(s) of sensing used, and the depth of collaborative interaction in the processing. By ?biometric source ? we mean the property of the person that is used for identification, such as fingerprint, voice, face appearance or iris texture. By type of sensing we mean different sensor modalities, such as 2D, 3D, or infra-red. By collaboration we mean the degree to which the processing of one biometric is influenced by the results of processing other biometrics. One common category of multi-modal biometrics might be called orthogonal. In this category, the biometric sources are different, such as face plus fingerprint used as a multi-modal biometric or a multi-biometric. In this category, there appears to be little or no opportunity for interaction between the processing of the individual biometrics. Another common category of multi-modal biometrics might be called independent. This type of processing is common with different modalities of sensing the face. For example, the 2D image of the face and the 3D shape of the face might be processed independently as biometrics, and then two results combined at a score or rank level. A less common category of multi-modal biometrics might be called collaborative. In this category, the processing of each individual biometric may be influenced by the other biometrics. For example, if specular highlights are found in the 2D face image, this might inform the processing of the 3D shape of the face, since specular highlights in the 2D often result in artifacts in the 3D shape. It is argued that the area of collaborative processing among multi-modal biometrics, although relatively less explored, holds the potential for important gains in accuracy.